51 citations · 117 across the 24 of their papers we have counts for
24 papers
KING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots
Taku Okawara, Aoki Takanose, Kenji Koide +2
Kinematic models provide reliable motion constraints for odometry estimation in featureless environments, where exteroceptive sensing degrades and IMU integration drifts. Learning-…
Image-to-Point Cloud Registration Made Easy with Rectified Flow-based LiDAR Upsampling
Reon Tabata, Kenji Koide, Shuji Oishi +4
Image-to-Point Cloud Registration (I2P) is essential for integrating camera and LiDAR in perception and autonomous systems, yet the modality gap between images and point clouds mak…
D-SLAMSpoof: An Environment-Agnostic LiDAR Spoofing Attack using Dynamic Point Cloud Injection
Rokuto Nagata, Kenji Koide, Kazuma Ikeda +2
In this work, we introduce Dynamic SLAMSpoof (D-SLAMSpoof), a novel attack that compromises LiDAR SLAM even in feature-rich environments. The attack leverages LiDAR spoofing, which…
MirrorDrift: Actuated Mirror-Based Attacks on LiDAR SLAM
Rokuto Nagata, Kenji Koide, Kazuma Ikeda +3
LiDAR SLAM provides high-accuracy localization but is fragile to point-cloud corruption because scan matching assumes geometric consistency. Prior physical attacks on LiDAR SLAM la…
Tightly-Coupled LiDAR-IMU-Leg Odometry with Online Learned Leg Kinematics Incorporating Foot Tactile Information
Taku Okawara, Kenji Koide, Aoki Takanose +4
In this letter, we present tightly coupled LiDAR-IMU-leg odometry, which is robust to challenging conditions such as featureless environments and deformable terrains. We developed…
Tightly Coupled Range Inertial Odometry and Mapping with Exact Point Cloud Downsampling
Kenji Koide, Aoki Takanose, Shuji Oishi +1
In this work, to facilitate the real-time processing of multi-scan registration error minimization on factor graphs, we devise a point cloud downsampling algorithm based on coreset…
Range-based 6-DoF Monte Carlo SLAM with Gradient-guided Particle Filter on GPU
Takumi Nakao, Kenji Koide, Aoki Takanose +3
This paper presents range-based 6-DoF Monte Carlo SLAM with a gradient-guided particle update strategy. While non-parametric state estimation methods, such as particle filters, are…
SLAMSpoof: Practical LiDAR Spoofing Attacks on Localization Systems Guided by Scan Matching Vulnerability Analysis
Rokuto Nagata, Kenji Koide, Yuki Hayakawa +6
Accurate localization is essential for enabling modern full self-driving services. These services heavily rely on map-based traffic information to reduce uncertainties in recognizi…
GLIM: 3D Range-Inertial Localization and Mapping with GPU-Accelerated Scan Matching Factors
Kenji Koide, Masashi Yokozuka, Shuji Oishi +1
This article presents GLIM, a 3D range-inertial localization and mapping framework with GPU-accelerated scan matching factors. The odometry estimation module of GLIM employs a comb…
Tightly-Coupled LiDAR-IMU-Wheel Odometry with an Online Neural Kinematic Model Learning via Factor Graph Optimization
Taku Okawara, Kenji Koide, Shuji Oishi +4
Environments lacking geometric features (e.g., tunnels and long straight corridors) are challenging for LiDAR-based odometry algorithms because LiDAR point clouds degenerate in suc…
MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents a 6-DoF range-based Monte Carlo localization method with a GPU-accelerated Stein particle filter. To update a massive amount of particles, we propose a Gauss-Ne…
Tightly-Coupled LiDAR-IMU-Wheel Odometry with Online Calibration of a Kinematic Model for Skid-Steering Robots
Taku Okawara, Kenji Koide, Shuji Oishi +4
Tunnels and long corridors are challenging environments for mobile robots because a LiDAR point cloud should degenerate in these environments. To tackle point cloud degeneration, t…
Tightly Coupled Range Inertial Localization on a 3D Prior Map Based on Sliding Window Factor Graph Optimization
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents a range inertial localization algorithm for a 3D prior map. The proposed algorithm tightly couples scan-to-scan and scan-to-map point cloud registration factors…
3D-BBS: Global Localization for 3D Point Cloud Scan Matching Using Branch-and-Bound Algorithm
Koki Aoki, Kenji Koide, Shuji Oishi +3
This paper presents an accurate and fast 3D global localization method, 3D-BBS, that extends the existing branch-and-bound (BnB)-based 2D scan matching (BBS) algorithm. To reduce m…
Exact Point Cloud Downsampling for Fast and Accurate Global Trajectory Optimization
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents a point cloud downsampling algorithm for fast and accurate trajectory optimization based on global registration error minimization. The proposed algorithm selec…
Single-Shot Global Localization via Graph-Theoretic Correspondence Matching
Shigemichi Matsuzaki, Kenji Koide, Shuji Oishi +2
This paper describes a method of global localization based on graph-theoretic association of instances between a query and the prior map. The proposed framework employs corresponde…
General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents an open source LiDAR-camera calibration toolbox that is general to LiDAR and camera projection models, requires only one pairing of LiDAR and camera data withou…
Generalized LOAM: LiDAR Odometry Estimation with Trainable Local Geometric Features
Kohei Honda, Kenji Koide, Masashi Yokozuka +2
This paper presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes…
Scalable Fiducial Tag Localization on a 3D Prior Map via Graph-Theoretic Global Tag-Map Registration
Kenji Koide, Shuji Oishi, Masashi Yokozuka +1
This paper presents an accurate and scalable method for fiducial tag localization on a 3D prior environmental map. The proposed method comprises three steps: 1) visual odometry-bas…
Globally Consistent and Tightly Coupled 3D LiDAR Inertial Mapping
Kenji Koide, Masashi Yokozuka, Shuji Oishi +1
This paper presents a real-time 3D mapping framework based on global matching cost minimization and LiDAR-IMU tight coupling. The proposed framework comprises a preprocessing modul…
Globally Consistent 3D LiDAR Mapping with GPU-accelerated GICP Matching Cost Factors
Kenji Koide, Masashi Yokozuka, Shuji Oishi +1
This paper presents a real-time 3D LiDAR mapping framework based on global matching cost minimization. The proposed method constructs a factor graph that directly minimizes matchin…
Adaptive Hyperparameter Tuning for Black-box LiDAR Odometry
Kenji Koide, Masashi Yokozuka, Shuji Oishi +1
This study proposes an adaptive data-driven hyperparameter tuning framework for black-box 3D LiDAR odometry algorithms. The proposed framework comprises offline parameter-error fun…
4D Attention: Comprehensive Framework for Spatio-Temporal Gaze Mapping
Shuji Oishi, Kenji Koide, Masashi Yokozuka +1
This study presents a framework for capturing human attention in the spatio-temporal domain using eye-tracking glasses. Attention mapping is a key technology for human perceptual a…
LiTAMIN2: Ultra Light LiDAR-based SLAM using Geometric Approximation applied with KL-Divergence
Masashi Yokozuka, Kenji Koide, Shuji Oishi +1
In this paper, a three-dimensional light detection and ranging simultaneous localization and mapping (SLAM) method is proposed that is available for tracking and mapping with 500--…